Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation

Abstract

With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the fundamental challenge of group recommendation is to model the correlations among members. Existing methods mostly adopt heuristic or attention-based preference aggregation strategies to synthesize group preferences. However, these models mainly focus on the pairwise connections of users and ignore the complex high-order interactions within and beyond groups. Besides, group recommendation suffers seriously from the problem of data sparsity due to severely sparse group-item interactions. In this paper, we propose a self-supervised hypergraph learning framework for group recommendation to achieve two goals: (1) capturing the intra- and inter-group interactions among users; (2) alleviating the data sparsity issue with the raw data itself. Technically, for (1), a hierarchical hypergraph convolutional network based on the user- and group-level hypergraphs is developed to model the complex tuplewise correlations among users within and beyond groups. For (2), we design a double-scale node dropout strategy to create self-supervision signals that can regularize user representations with different granularities against the sparsity issue. The experimental analysis on multiple benchmark datasets demonstrates the superiority of the proposed model and also elucidates the rationality of the hypergraph modeling and the double-scale self-supervision.

11 pages, 6 figures, CIKM 2021

References in corpus (2)

• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization

Cited by in corpus (11)

• Attention-based graph neural networks: a survey
• Contrastive Collaborative Filtering for Cold-Start Item Recommendation
• A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide
• ConsRec: Learning Consensus Behind Interactions for Group Recommendation
• A Comprehensive Survey on Self-Supervised Learning for Recommendation
• AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
• Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
• Enhancing Hyperedge Prediction with Context-Aware Self-Supervised Learning
• Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for Recommendations
• Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations
• HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction